EDITORIAL article
Front. Hum. Neurosci.
Sec. Brain Health and Clinical Neuroscience
This article is part of the Research TopicAI Innovations in Neurological and Psychiatric Disorder Management: Diagnosis to TreatmentView all 6 articles
Editorial: AI Innovations in Neurological and Psychiatric Disorder Management: Diagnosis to Treatment
Provisionally accepted
- 1School of Information Engineering, Nanchang University, Nanchang, China
- 2Affiliated Rehabilitation Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- 3Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
- 4Jiangxi Cancer Hospital, Nanchang, China
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Neurological and psychiatric disorders affect over 1 billion people globally, posing immense health, economic, and social burdens. From neurodevelopmental conditions (ADHD, dyslexia) to cerebrovascular events (stroke), age-related cognitive impairment (MCI), and traumatic brain injury (TBI), these disorders share core challenges: complex pathogenesis, heterogeneous presentations, delayed diagnosis, and limited personalized care. Traditional subjective assessments and one-size-fitsall interventions often yield suboptimal outcomes, creating an urgent need for innovative solutions.Artificial intelligence (AI)-encompassing machine learning, deep learning, and data analyticshas emerged as a transformative tool, harnessing multi-modal data (neuroimaging, clinical metrics, behavioral assessments) to enable precise pattern recognition, predictive modeling, and adaptive interventions. This Research Topic gathers five cutting-edge studies translating AI advancements into clinical practice across diverse brain disorders. Below, we synthesize their core contributions, contextualizing their significance in advancing AI-driven brain health. Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications (Wang et al., 2025) The five studies in this Research Topic collectively showcase the breadth and depth of AI's potential to transform neurological and psychiatric care. From mapping global research trends in ADHD and optimizing stroke care pathways, to enabling early MCI screening, personalizing TBI management, and advancing dyslexia interventions, each contribution aligns with the Topic's core mission: leveraging datadriven innovation to address unmet clinical needs. Together, they highlight three key themes: the power of multi-modal data integration, the importance of personalized care, and the need to bridge researchpractice gaps for equitable AI translation.
Keywords: artificial intelligence, Diagnosis and treatment, Disorder Management, Neurological Disorder, Psychiatric disorder
Received: 03 Dec 2025; Accepted: 10 Dec 2025.
Copyright: © 2025 Hong, Bai, Sui and Jian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jin Hong
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.